BP神经网络

import math
from pandas import DataFrame

def sigmoid(x): #映射函数
    return 1/(1+math.exp(-x))

x1=[0.29,0.50,0.00,0.21,0.10,0.06,0.13,0.24,0.28]
x2=[0.23,0.62,0.53,0.53,0.33,0.15,0.03,0.23,0.03]
y=[0.14,0.64,0.28,0.33,0.12,0.03,0.02,0.11,0.08]
yita=0.1

for i in range(9):
    Net_in =DataFrame(0.6,index=['input1','input2','theata'],columns=['a'])
    Out_in = DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
    Net_in.loc['input1'] =x1[i]
    Net_in.loc['input2']=x2[i]
    real=y[i]
    Net_in.loc['theata'] = -1
    Out_in.loc['theata'] = -1
    W_mid=DataFrame(0.7,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])
    W_out=DataFrame(0.7,index=['input1','input2','input3','input4','theata'],columns=['a'])
    W_mid_delta=DataFrame(0,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])
    W_out_delta=DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
    for i in range(0,4):
        Out_in.iloc[i,0] = sigmoid(sum(W_mid.iloc[:,i]*Net_in.iloc[:,0]))

#输出层的输出/网络输出
    res = sigmoid(sum(Out_in.iloc[:,0]*W_out.iloc[:,0]))
    error = abs(res-real)
    W_out_delta.iloc[:,0] = yita*res*(1-res)*(real-res)*Out_in.iloc[:,0]
    W_out_delta.iloc[4,0] = -(yita*res*(1-res)*(real-res))
    W_out = W_out + W_out_delta #输出层权值更新
    for i in range(0,4):
        W_mid_delta.iloc[:,i] = yita*Out_in.iloc[i,0]*(1-Out_in.iloc[i,0])*W_out.iloc[i,0]*res*(1-res)*(real-res)*Net_in.iloc[:,0]
        W_mid_delta.iloc[2,i] = -(yita*Out_in.iloc[i,0]*(1-Out_in.iloc[i,0])*W_out.iloc[i,0]*res*(1-res)*(real-res))
    W_mid = W_mid + W_mid_delta #中间层权值更新
testx1=[0.38,0.29]
testx2=[0.49,0.47]
for i in range(2):
    Net_in =DataFrame(0.6,index=['input1','input2','theata'],columns=['a'])
    Out_in = DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
    Net_in.loc['input1'] =testx1[i]
    Net_in.loc['input2']=testx2[i]
    Net_in.loc['theata'] = -1
    Out_in.loc['theata'] = -1
    for i in range(0,4):
        Out_in.iloc[i,0] = sigmoid(sum(W_mid.iloc[:,i]*Net_in.iloc[:,0]))

#输出层的输出/网络输出
    res = sigmoid(sum(Out_in.iloc[:,0]*W_out.iloc[:,0]))
    print(res)